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Analysis of tremor in motor learning task

Analysis of tremor in motor learning task Hand-eye coordination is required in many skilled tasks. Individual differences affect the performance of people at work and leisure, for example, during assembly jobs or sports. The aim of this study was to determine whether motor learning can change the physiological action of hand tremor. Tremor is a repetitive and stereotyped movement, with regular frequency and amplitude, but there are different types of tremors with pattern variation. The results were calculated by participants' timeon and time-off the target, the average distance from the center of the target, and the frequency of oscillatory movement of a cursor (tremor). The results of this study indicate a statistically significant (p < 0.05) influence of effect of task repetition on improvement of motor control and reduction of a high-amplitude tremor and an increase of a low-amplitude tremor. The assessed individuals achieved more than 50% better outcomes of a hand-eye coordination task in the final trials when compared with the initial trials. The dynamics of motor learning tend to rise, with a steady level of a 1-h interval between trials. Keywords: hand-eye coordination; hand tremor; motor learning task. analyzed tremors are essential tremor (ET), Parkinson's disease tremor, and dystonic tremor [3]. Tremor can be classified as resting or action (kinetic, intention) tremor. Resting tremor occurs when the affected body part is not active and is supported against gravity, whereas action tremor occurs during voluntary muscle activation and includes kinetic tremor (occurs in both goal-directed and non-goal-directed movements) and intention tremor (characterized by an increase in tremor amplitude as the target is approached [4]). Action tremor occurs on performing a voluntary, purposeful (intended) movement, such as writing or pressing a button [5­7]. This tremor will often disappear while the affected body part is at rest [8]. The pattern of action tremor varies in different conditions. Tremor is classified in several ways, but the most basic is differentiation between physiological and pathological tremor [9]. Objectives The aim of this study was to determine whether motor learning can influence the physiological action of hand tremor. In one study, it was found that visuomotor skill training produces a general reduction in finger tremor (pulsatile control) during voluntary movements [10]. Another study found that healthy elderly individuals with higher frequencies tremor (7.6­12.5 Hz) were not at an advantage over other healthy elderly individuals when performing a pronation-supination task [11]. Several studies have reported that tremor power in the 8­12Hz range is not influenced by alteration of visual feedback and training [12, 13]. *Corresponding author: Piotr Walecki, Jagiellonian University Medical College, Krakow, Poland , E-mail: piotr.walecki@uj.edu.pl Wojciech Laso: Jagiellonian University Medical College, Krakow, Poland Marek Kunc: University of Leeds, Leeds, West Yorkshire, UK Edward J. Gorzelaczyk: Polish Academy of Sciences, Warsaw, Poland Introduction Tremor is the most common characteristic of movement, and it is defined as a fine, rhythmic involuntary oscillation of a body part that occurs in a back-and-forth pattern. It can vary in frequency and amplitude and is influenced by physiological or psychological factors, for example, stress, fatigue, anger, fear, consumption of caffeine and other chemical substances [1]. Tremor is a repetitive and stereotyped movement, with regular frequency and amplitude, but there are different types of tremors with pattern variation [2]. In clinical practice, the most frequently Materials and methods In this paper, physiological hand tremor was analyzed. The PEBL pursuit rotor task (PRT) was used. The PRT is a visual-manual tracking task tool assessing dexterity. It is commonly used in an evaluation of hand-eye coordination and motor learning. The main performance measure is total time on-target (TOT) and number of stylus-target contacts (HITS) [14]. 46Walecki etal.: Analysis of tremor in motor learning task Distance from target Figure 1An example of changing the distance (in pixels) between the mouse pointer and the center of the stimulus at a time of 15,000 ms. The PEBL PRT has been released into the public domain [15, 16]: this is a computer implementation of physical equipment (a turntable-like platter with a metal spot on it) to study motor performance, popular in the mid-20th century (e.g., Lafayette Instruments 30014). Originally, individuals held a wand with a metal tip trying to follow a small disc moving quickly on a turntable. A circuit was completed and a timer activated. The speed of rotation may vary, but most frequently physical tasks operated at a rate of 60rpm (1/s), which is too fast for mouse-controlled versions of PEBL PRT [15, 16]. In the PEBL PRT, speed (rotations/s) is 1/7.5 and sampling rate is at least 1/10 ms. The experiment is set to run with a screen resolution of 800 × 600 pixels with an aspect ratio of 4 × 3. The PEBL PRT is relatively stable with practice and unaffected by time of day. Results showed that a type II analysis of variance (ANOVA) of the PRT mean offset (a measure of pixel deviation from the target) revealed that there was no reliable effect of time of day, F(3,68) = 0.4712, p = 0.7034, nor of session block, F(2,68) = 0.05, p = 0.94 [17]. The PEBL version offers four trials and controllable parameters that can be used as a test of hand-eye coordination. We modified the PEBL PRT and created a special program for advanced analysis of the results, which computes more than 250 dynamic parameters of motion (such as tremor amplitude and frequency, change of distance from the target, etc.). This provides an opportunity to measure procedural memory as well as demonstrating the participant's fine-motor skills. The results are calculated by the participant's time-on and time-off the object. During the PRT individuals follow a red dot with the curser on a circular path for 2 min. The longer a participant can keep the curser on the dot as it follows a circular path, the higher the cognitive score. There are four trials, which means that the entire test can be performed in up to 2 min. The task was conducted twice by each Walecki etal.: Analysis of tremor in motor learning task47 Table 1Repeated measurement analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.9691 0.4824 Noncentrality 1944.541 404.429 Observed power ( = 0.05) 1.000000 1.000000 Variable `distance' (dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. Table 2Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.990614 0.460828 Noncentrality 6543.881 370.938 Observed power ( = 0.05) 1.000000 1.000000 Variable `TOT' (dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. Table 3Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.965077 0.518841 Noncentrality 1713.322 467.989 Observed power ( = 0.05) 1.000000 1.000000 Variable `tremor' (dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. individual at approximately 1-h intervals. The study was performed separately for both dominant and non-dominant hands. Rotation speed is 1 rotation every 7.5 s, for two rotations/trials. In the task, the color of the target changes from dark red to bright red when the cursor is LS Means variable DISTANCE (dominant hand) Current effect: F(7, 434)=57.776, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals on top of it, providing extra feedback on performance [15­17]. The study involved 63 individuals aged 20years ( ± 1.12). The study was conducted at the Jagiellonian University Medical College in Krakow, Poland. LS Means variable TOT (dominant hand) Current effect: F(7, 434)=52.991, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 95 Total time on-target [percentage] 90 85 80 75 70 65 One-hour interval 1 2 3 4 Trials 5 6 7 8 Distance [pixels] One-hour interval 2 3 4 Trials 5 6 7 8 Figure 2The average distance in pixels from the center of the target calculated for eight trials for the dominant hand. Figure 3The total time on-target calculated for eight trials for the dominant hand. 48Walecki etal.: Analysis of tremor in motor learning task LS Means variable TREMOR (dominant hand) Current effect: F(7, 434)=66.856, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 14 13 12 Tremor [Hz] tremor ­ the frequency of oscillatory movement of a mouse pointer, calculated for an amplitude of 1­10 pixels. 11 10 9 8 7 1 2 3 One-hour interval 4 Trials 5 6 7 8 Results In Tables 1­3 (and Figures 2­4), the results of ANOVA with repeated measurements for three variables (distance, TOT, tremor) measured for the dominant hand are presented. Because ANOVA F-tests indicated the existence of statistically significant differences between means for all variables, post-hoc tests were performed to determine which mean differences were statistically significant (Tables 4­6). In Tables 7­9 (and Figures 5­7), the results of ANOVA with repeated measurements for three variables (distance, TOT, tremor) measured for the non-dominant hand are presented. Because ANOVA F-tests indicated the existence of statistically significant differences between means for all variables, post-hoc tests were performed to determine which mean differences were statistically significant (Tables 10­12). The results were calculated by the participants' time-on and time-off the target, the average distance from the center Figure 4The frequency of oscillatory movement of a mouse pointer calculated for eight trials for the dominant hand. During the performance of the PEBL PRT hand tremor was measured. Motor learning was described by changes in three variables: ­ distance ­ the average distance in pixels from the center of the target, calculated for a time of 15,000ms (Figure 1); ­ TOT ­ the total time on-target, is a whole time in milliseconds when the cursor was present in the target area; Table 4Probabilities for post-hoc tests. Distance {1} 21.743 {2} 18.416 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 {3} 16.257 0.0000 0.0000 0.1258 0.7645 0.0142 0.0024 0.0004 {4} 15.580 0.0000 0.0000 0.1258 0.0674 0.3535 0.1288 0.0455 {5} 16.390 0.0000 0.0000 0.7645 0.0674 0.0060 0.0009 0.0001 {6} 15.169 0.0000 0.0000 0.0142 0.3535 0.0060 0.5534 0.2822 {7} 14.907 0.0000 0.0000 0.0024 0.1288 0.0009 0.5534 0.6290 {8} 14.693 0.0000 0.0000 0.0004 0.0455 0.0001 0.2822 0.6290 LSD test, variable: `distance' (dominant hand). Error: between MS = 6.1504, df = 434.00. Table 5Probabilities for post-hoc tests. TOT {1} 71.429 {2} 79.877 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0002 0.0000 0.0000 0.0000 {3} 84.217 0.0000 0.0001 0.0721 0.8836 0.0053 0.0006 0.0001 {4} 86.209 0.0000 0.0000 0.0721 0.0519 0.3176 0.0972 0.0430 {5} 84.055 0.0000 0.0002 0.8836 0.0519 0.0034 0.0003 0.0001 {6} 87.315 0.0000 0.0000 0.0053 0.3176 0.0034 0.5086 0.3040 {7} 88.046 0.0000 0.0000 0.0006 0.0972 0.0003 0.5086 0.7135 {8} 88.452 0.0000 0.0000 0.0001 0.0430 0.0001 0.3040 0.7135 LSD test, variable: `TOT' (dominant hand). Error: between MS = 38.452, df = 434.00. Walecki etal.: Analysis of tremor in motor learning task49 Table 6Probabilities for post-hoc tests. Tremor {1} 8.3556 {2} 9.9228 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 {3} 11.047 0.0000 0.0000 0.1353 0.0038 0.0000 0.0000 0.0000 {4} 11.422 0.0000 0.0000 0.1353 0.1587 0.0034 0.0000 0.0000 {5} 11.777 0.0000 0.0000 0.0038 0.1587 0.1268 0.0014 0.0026 {6} 12.161 0.0000 0.0000 0.0000 0.0034 0.1268 0.0917 0.1353 {7} 12.585 0.0000 0.0000 0.0000 0.0000 0.0014 0.0917 0.8464 {8} 12.537 0.0000 0.0000 0.0000 0.0000 0.0026 0.1353 0.8464 LSD test, variable: `TOT' (dominant hand). Error: between MS = 38.452, df = 434.00. Table 7Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.945198 0.384403 Noncentrality 741.6389 187.9564 Observed power ( = 0.05) 1.000000 1.000000 Variable `distance' (non-dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. Table 8Repeated measurements analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.952837 0.369281 Noncentrality 868.7364 176.2328 Observed power ( = 0.05) 1.000000 1.000000 Variable TOT (non-dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. of the target, and the frequency of oscillatory movement of a cursor (tremor). The results of this study indicate a statistically significant (p<0.05) influence of effect of task repetition on improvement of motor control and reduction of a high-amplitude tremor and an increase of a low-amplitude tremor. The assessed individuals achieved more than 50% better results on psychomotor performance in a hand-eye coordination task when first and last trials were compared. The dynamics of motor learning tends to rise, with a steady level of a 1-h interval between trials. Table 9Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.926499 0.310564 Noncentrality 542.0221 135.5888 Observed power ( = 0.05) 1.000000 1.000000 Variable `tremor' (non-dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. 50Walecki etal.: Analysis of tremor in motor learning task LS Means variable DISTANCE (non-dominant hand) Current effect: F(7, 301)=26.851, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 50 45 Distance [pixels] 40 35 30 25 20 15 1 2 3 One-hour interval 4 Trials 5 6 7 8 Discussion Methodologies and measures are essential to an understanding of motor learning. In the 20th century researchers developed several tools to estimate motor learning. The ability of humans to make complex hand movements is required in many skilled tasks. Of particular interest is the learning of hand-eye coordination. A commonly used task in motor learning studies is the pursuit rotor. This simple task can be set-up and used in a basic motor learning study and can illustrate a change in pursuit-rotor performance, which can be regarded as a measure of hand-eye coordination functioning and procedural memory. Individual differences in the performance of people at work or leisure are related to lack of sleep and drug use. This test can also be used as a diagnostic tool to identify some neurological disorders [18­20 ]. In this study, we added a new parameter ­ that hand tremor is associated with level of motor skill. We evaluated several dozen trials to estimate the relationship between change in performance (motor learning) and level of hand tremor. Tremor measurement is an important part of the PRT test, contributing to a deeper understanding of assessment if no other impairment is shown during the motor task. In this way, we combined analysis of psychological characteristics (learning and memory) with analysis of physiological features (tremor), and as a result created a better measurement of fine-motor skills. Through the use of a computer version of the PRT, pursuit rotor performance can be practically operationalized very well. We analyzed visual-motor tracking skills and hand-eye coordination by requiring the participant to follow a moving object with a cursor, and to follow the target on a computer screen. Computer analyses allow precise measurement of distance (from the center of the target) and tremor (the frequency of oscillatory movement of a mouse pointer), and may be used after calculating additional parameters of movement in a more complex psychophysiological analysis [18, 21]. Figure 5The average distance in pixels from the center of the target calculated for eight trials for the non-dominant hand. LS Means variable TOT (non-dominant hand) Current effect: F(7, 301)=25.176, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 75 Total time on-target [percentage] 70 65 60 55 50 45 40 35 30 1 2 3 4 Trials 5 6 7 8 One-hour interval Figure 6The total time on-target calculated for eight trials for the non-dominant hand. LS Means variable TREMOR (non-dominant hand) Current effect: F(7, 301)=19.370, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 1 2 3 Tremor [Hz] One-hour interval 4 Trials 5 6 7 8 Conclusion In this study, the main aim was to test a new method and to evaluate whether motor learning can influence hand tremor. The use of the proposed method can lead to a more precise measurement of motor skills and can be Figure 7The frequency of oscillatory movement of a mouse pointer calculated for eight trials for the non-dominant hand. Walecki etal.: Analysis of tremor in motor learning task51 Table 10Probabilities for post-hoc tests. Distance {1} 39.148 {2} 31.562 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0163 0.0006 0.0014 0.0000 0.0000 0.0000 {3} 27.901 0.0000 0.0163 0.2934 0.4180 0.0081 0.0022 0.0001 {4} 26.307 0.0000 0.0006 0.2934 0.8094 0.1074 0.0433 0.0051 {5} 26.672 0.0000 0.0014 0.4180 0.8094 0.0644 0.0239 0.0024 {6} 23.860 0.0000 0.0000 0.0081 0.1074 0.0644 0.6789 0.2283 {7} 23.232 0.0000 0.0000 0.0022 0.0433 0.0239 0.6789 0.4285 {8} 22.030 0.0000 0.0000 0.0001 0.0051 0.0024 0.2283 0.4285 LSD test, variable: `distance' (non-dominant hand). Error: between MS = 50.513, df = 301.00. Table 11Probabilities for post-hoc tests. TOT {1} 43.337 {2} 52.739 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0492 0.0048 0.0241 0.0000 0.0000 0.0000 {3} 57.106 0.0000 0.0492 0.3882 0.7711 0.0023 0.0002 0.0000 {4} 59.017 0.0000 0.0048 0.3882 0.5671 0.0275 0.0037 0.0001 {5} 57.750 0.0000 0.0241 0.7711 0.5671 0.0056 0.0005 0.0000 {6} 63.916 0.0000 0.0000 0.0023 0.0275 0.0056 0.4802 0.0779 {7} 65.479 0.0000 0.0000 0.0002 0.0037 0.0005 0.4802 0.2889 {8} 67.828 0.0000 0.0000 0.0000 0.0001 0.0000 0.0779 0.2889 LSD test, variable: `TOT' (non-dominant hand). Error: between MS = 107.57, df = 301.00. Table 12Probabilities for post-hoc tests. Tremor {1} 5.2795 {2} 6.3818 0.0012 0.0012 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0231 0.1645 0.0281 0.0000 0.0000 0.0000 {3} 7.1515 0.0000 0.0231 0.3742 0.9391 0.0355 0.0066 0.0000 {4} 6.8515 0.0000 0.1645 0.3742 0.4165 0.0029 0.0003 0.0000 {5} 7.1258 0.0000 0.0281 0.9391 0.4165 0.0294 0.0052 0.0000 {6} 7.8636 0.0000 0.0000 0.0355 0.0029 0.0294 0.5326 0.0253 {7} 8.0742 0.0000 0.0000 0.0066 0.0003 0.0052 0.5326 0.1057 {8} 8.6212 0.0000 0.0000 0.0000 0.0000 0.0000 0.0253 0.1057 LSD test, variable: `Tremor' (non-dominant hand). Error: between MS = 2.4999, df = 301.00. operationalized better. The study showed improvement in procedural task and the relationship between tremor level and performance level. The better performance was related to reduction of large-amplitude and low-frequency tremor and increased low-amplitude and high-frequency tremor. Received December 18, 2012; revised January 25, 2013; accepted January 28, 2013 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bio-Algorithms and Med-Systems de Gruyter

Analysis of tremor in motor learning task

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de Gruyter
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1895-9091
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1896-530X
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10.1515/bams-2013-0006
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Abstract

Hand-eye coordination is required in many skilled tasks. Individual differences affect the performance of people at work and leisure, for example, during assembly jobs or sports. The aim of this study was to determine whether motor learning can change the physiological action of hand tremor. Tremor is a repetitive and stereotyped movement, with regular frequency and amplitude, but there are different types of tremors with pattern variation. The results were calculated by participants' timeon and time-off the target, the average distance from the center of the target, and the frequency of oscillatory movement of a cursor (tremor). The results of this study indicate a statistically significant (p < 0.05) influence of effect of task repetition on improvement of motor control and reduction of a high-amplitude tremor and an increase of a low-amplitude tremor. The assessed individuals achieved more than 50% better outcomes of a hand-eye coordination task in the final trials when compared with the initial trials. The dynamics of motor learning tend to rise, with a steady level of a 1-h interval between trials. Keywords: hand-eye coordination; hand tremor; motor learning task. analyzed tremors are essential tremor (ET), Parkinson's disease tremor, and dystonic tremor [3]. Tremor can be classified as resting or action (kinetic, intention) tremor. Resting tremor occurs when the affected body part is not active and is supported against gravity, whereas action tremor occurs during voluntary muscle activation and includes kinetic tremor (occurs in both goal-directed and non-goal-directed movements) and intention tremor (characterized by an increase in tremor amplitude as the target is approached [4]). Action tremor occurs on performing a voluntary, purposeful (intended) movement, such as writing or pressing a button [5­7]. This tremor will often disappear while the affected body part is at rest [8]. The pattern of action tremor varies in different conditions. Tremor is classified in several ways, but the most basic is differentiation between physiological and pathological tremor [9]. Objectives The aim of this study was to determine whether motor learning can influence the physiological action of hand tremor. In one study, it was found that visuomotor skill training produces a general reduction in finger tremor (pulsatile control) during voluntary movements [10]. Another study found that healthy elderly individuals with higher frequencies tremor (7.6­12.5 Hz) were not at an advantage over other healthy elderly individuals when performing a pronation-supination task [11]. Several studies have reported that tremor power in the 8­12Hz range is not influenced by alteration of visual feedback and training [12, 13]. *Corresponding author: Piotr Walecki, Jagiellonian University Medical College, Krakow, Poland , E-mail: piotr.walecki@uj.edu.pl Wojciech Laso: Jagiellonian University Medical College, Krakow, Poland Marek Kunc: University of Leeds, Leeds, West Yorkshire, UK Edward J. Gorzelaczyk: Polish Academy of Sciences, Warsaw, Poland Introduction Tremor is the most common characteristic of movement, and it is defined as a fine, rhythmic involuntary oscillation of a body part that occurs in a back-and-forth pattern. It can vary in frequency and amplitude and is influenced by physiological or psychological factors, for example, stress, fatigue, anger, fear, consumption of caffeine and other chemical substances [1]. Tremor is a repetitive and stereotyped movement, with regular frequency and amplitude, but there are different types of tremors with pattern variation [2]. In clinical practice, the most frequently Materials and methods In this paper, physiological hand tremor was analyzed. The PEBL pursuit rotor task (PRT) was used. The PRT is a visual-manual tracking task tool assessing dexterity. It is commonly used in an evaluation of hand-eye coordination and motor learning. The main performance measure is total time on-target (TOT) and number of stylus-target contacts (HITS) [14]. 46Walecki etal.: Analysis of tremor in motor learning task Distance from target Figure 1An example of changing the distance (in pixels) between the mouse pointer and the center of the stimulus at a time of 15,000 ms. The PEBL PRT has been released into the public domain [15, 16]: this is a computer implementation of physical equipment (a turntable-like platter with a metal spot on it) to study motor performance, popular in the mid-20th century (e.g., Lafayette Instruments 30014). Originally, individuals held a wand with a metal tip trying to follow a small disc moving quickly on a turntable. A circuit was completed and a timer activated. The speed of rotation may vary, but most frequently physical tasks operated at a rate of 60rpm (1/s), which is too fast for mouse-controlled versions of PEBL PRT [15, 16]. In the PEBL PRT, speed (rotations/s) is 1/7.5 and sampling rate is at least 1/10 ms. The experiment is set to run with a screen resolution of 800 × 600 pixels with an aspect ratio of 4 × 3. The PEBL PRT is relatively stable with practice and unaffected by time of day. Results showed that a type II analysis of variance (ANOVA) of the PRT mean offset (a measure of pixel deviation from the target) revealed that there was no reliable effect of time of day, F(3,68) = 0.4712, p = 0.7034, nor of session block, F(2,68) = 0.05, p = 0.94 [17]. The PEBL version offers four trials and controllable parameters that can be used as a test of hand-eye coordination. We modified the PEBL PRT and created a special program for advanced analysis of the results, which computes more than 250 dynamic parameters of motion (such as tremor amplitude and frequency, change of distance from the target, etc.). This provides an opportunity to measure procedural memory as well as demonstrating the participant's fine-motor skills. The results are calculated by the participant's time-on and time-off the object. During the PRT individuals follow a red dot with the curser on a circular path for 2 min. The longer a participant can keep the curser on the dot as it follows a circular path, the higher the cognitive score. There are four trials, which means that the entire test can be performed in up to 2 min. The task was conducted twice by each Walecki etal.: Analysis of tremor in motor learning task47 Table 1Repeated measurement analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.9691 0.4824 Noncentrality 1944.541 404.429 Observed power ( = 0.05) 1.000000 1.000000 Variable `distance' (dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. Table 2Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.990614 0.460828 Noncentrality 6543.881 370.938 Observed power ( = 0.05) 1.000000 1.000000 Variable `TOT' (dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. Table 3Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.965077 0.518841 Noncentrality 1713.322 467.989 Observed power ( = 0.05) 1.000000 1.000000 Variable `tremor' (dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. individual at approximately 1-h intervals. The study was performed separately for both dominant and non-dominant hands. Rotation speed is 1 rotation every 7.5 s, for two rotations/trials. In the task, the color of the target changes from dark red to bright red when the cursor is LS Means variable DISTANCE (dominant hand) Current effect: F(7, 434)=57.776, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals on top of it, providing extra feedback on performance [15­17]. The study involved 63 individuals aged 20years ( ± 1.12). The study was conducted at the Jagiellonian University Medical College in Krakow, Poland. LS Means variable TOT (dominant hand) Current effect: F(7, 434)=52.991, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 95 Total time on-target [percentage] 90 85 80 75 70 65 One-hour interval 1 2 3 4 Trials 5 6 7 8 Distance [pixels] One-hour interval 2 3 4 Trials 5 6 7 8 Figure 2The average distance in pixels from the center of the target calculated for eight trials for the dominant hand. Figure 3The total time on-target calculated for eight trials for the dominant hand. 48Walecki etal.: Analysis of tremor in motor learning task LS Means variable TREMOR (dominant hand) Current effect: F(7, 434)=66.856, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 14 13 12 Tremor [Hz] tremor ­ the frequency of oscillatory movement of a mouse pointer, calculated for an amplitude of 1­10 pixels. 11 10 9 8 7 1 2 3 One-hour interval 4 Trials 5 6 7 8 Results In Tables 1­3 (and Figures 2­4), the results of ANOVA with repeated measurements for three variables (distance, TOT, tremor) measured for the dominant hand are presented. Because ANOVA F-tests indicated the existence of statistically significant differences between means for all variables, post-hoc tests were performed to determine which mean differences were statistically significant (Tables 4­6). In Tables 7­9 (and Figures 5­7), the results of ANOVA with repeated measurements for three variables (distance, TOT, tremor) measured for the non-dominant hand are presented. Because ANOVA F-tests indicated the existence of statistically significant differences between means for all variables, post-hoc tests were performed to determine which mean differences were statistically significant (Tables 10­12). The results were calculated by the participants' time-on and time-off the target, the average distance from the center Figure 4The frequency of oscillatory movement of a mouse pointer calculated for eight trials for the dominant hand. During the performance of the PEBL PRT hand tremor was measured. Motor learning was described by changes in three variables: ­ distance ­ the average distance in pixels from the center of the target, calculated for a time of 15,000ms (Figure 1); ­ TOT ­ the total time on-target, is a whole time in milliseconds when the cursor was present in the target area; Table 4Probabilities for post-hoc tests. Distance {1} 21.743 {2} 18.416 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 {3} 16.257 0.0000 0.0000 0.1258 0.7645 0.0142 0.0024 0.0004 {4} 15.580 0.0000 0.0000 0.1258 0.0674 0.3535 0.1288 0.0455 {5} 16.390 0.0000 0.0000 0.7645 0.0674 0.0060 0.0009 0.0001 {6} 15.169 0.0000 0.0000 0.0142 0.3535 0.0060 0.5534 0.2822 {7} 14.907 0.0000 0.0000 0.0024 0.1288 0.0009 0.5534 0.6290 {8} 14.693 0.0000 0.0000 0.0004 0.0455 0.0001 0.2822 0.6290 LSD test, variable: `distance' (dominant hand). Error: between MS = 6.1504, df = 434.00. Table 5Probabilities for post-hoc tests. TOT {1} 71.429 {2} 79.877 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0002 0.0000 0.0000 0.0000 {3} 84.217 0.0000 0.0001 0.0721 0.8836 0.0053 0.0006 0.0001 {4} 86.209 0.0000 0.0000 0.0721 0.0519 0.3176 0.0972 0.0430 {5} 84.055 0.0000 0.0002 0.8836 0.0519 0.0034 0.0003 0.0001 {6} 87.315 0.0000 0.0000 0.0053 0.3176 0.0034 0.5086 0.3040 {7} 88.046 0.0000 0.0000 0.0006 0.0972 0.0003 0.5086 0.7135 {8} 88.452 0.0000 0.0000 0.0001 0.0430 0.0001 0.3040 0.7135 LSD test, variable: `TOT' (dominant hand). Error: between MS = 38.452, df = 434.00. Walecki etal.: Analysis of tremor in motor learning task49 Table 6Probabilities for post-hoc tests. Tremor {1} 8.3556 {2} 9.9228 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 {3} 11.047 0.0000 0.0000 0.1353 0.0038 0.0000 0.0000 0.0000 {4} 11.422 0.0000 0.0000 0.1353 0.1587 0.0034 0.0000 0.0000 {5} 11.777 0.0000 0.0000 0.0038 0.1587 0.1268 0.0014 0.0026 {6} 12.161 0.0000 0.0000 0.0000 0.0034 0.1268 0.0917 0.1353 {7} 12.585 0.0000 0.0000 0.0000 0.0000 0.0014 0.0917 0.8464 {8} 12.537 0.0000 0.0000 0.0000 0.0000 0.0026 0.1353 0.8464 LSD test, variable: `TOT' (dominant hand). Error: between MS = 38.452, df = 434.00. Table 7Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.945198 0.384403 Noncentrality 741.6389 187.9564 Observed power ( = 0.05) 1.000000 1.000000 Variable `distance' (non-dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. Table 8Repeated measurements analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.952837 0.369281 Noncentrality 868.7364 176.2328 Observed power ( = 0.05) 1.000000 1.000000 Variable TOT (non-dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. of the target, and the frequency of oscillatory movement of a cursor (tremor). The results of this study indicate a statistically significant (p<0.05) influence of effect of task repetition on improvement of motor control and reduction of a high-amplitude tremor and an increase of a low-amplitude tremor. The assessed individuals achieved more than 50% better results on psychomotor performance in a hand-eye coordination task when first and last trials were compared. The dynamics of motor learning tends to rise, with a steady level of a 1-h interval between trials. Table 9Repeated measures analysis of variance with effect sizes and powers. SS df MS F p-Value Partial eta-squared 0.926499 0.310564 Noncentrality 542.0221 135.5888 Observed power ( = 0.05) 1.000000 1.000000 Variable `tremor' (non-dominant hand). Sigma-restricted parameterization. Effective hypothesis decomposition. 50Walecki etal.: Analysis of tremor in motor learning task LS Means variable DISTANCE (non-dominant hand) Current effect: F(7, 301)=26.851, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 50 45 Distance [pixels] 40 35 30 25 20 15 1 2 3 One-hour interval 4 Trials 5 6 7 8 Discussion Methodologies and measures are essential to an understanding of motor learning. In the 20th century researchers developed several tools to estimate motor learning. The ability of humans to make complex hand movements is required in many skilled tasks. Of particular interest is the learning of hand-eye coordination. A commonly used task in motor learning studies is the pursuit rotor. This simple task can be set-up and used in a basic motor learning study and can illustrate a change in pursuit-rotor performance, which can be regarded as a measure of hand-eye coordination functioning and procedural memory. Individual differences in the performance of people at work or leisure are related to lack of sleep and drug use. This test can also be used as a diagnostic tool to identify some neurological disorders [18­20 ]. In this study, we added a new parameter ­ that hand tremor is associated with level of motor skill. We evaluated several dozen trials to estimate the relationship between change in performance (motor learning) and level of hand tremor. Tremor measurement is an important part of the PRT test, contributing to a deeper understanding of assessment if no other impairment is shown during the motor task. In this way, we combined analysis of psychological characteristics (learning and memory) with analysis of physiological features (tremor), and as a result created a better measurement of fine-motor skills. Through the use of a computer version of the PRT, pursuit rotor performance can be practically operationalized very well. We analyzed visual-motor tracking skills and hand-eye coordination by requiring the participant to follow a moving object with a cursor, and to follow the target on a computer screen. Computer analyses allow precise measurement of distance (from the center of the target) and tremor (the frequency of oscillatory movement of a mouse pointer), and may be used after calculating additional parameters of movement in a more complex psychophysiological analysis [18, 21]. Figure 5The average distance in pixels from the center of the target calculated for eight trials for the non-dominant hand. LS Means variable TOT (non-dominant hand) Current effect: F(7, 301)=25.176, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 75 Total time on-target [percentage] 70 65 60 55 50 45 40 35 30 1 2 3 4 Trials 5 6 7 8 One-hour interval Figure 6The total time on-target calculated for eight trials for the non-dominant hand. LS Means variable TREMOR (non-dominant hand) Current effect: F(7, 301)=19.370, p=0.0000 Effective hypothesis decomposition Vertical bars denote 0.95 confidence intervals 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 1 2 3 Tremor [Hz] One-hour interval 4 Trials 5 6 7 8 Conclusion In this study, the main aim was to test a new method and to evaluate whether motor learning can influence hand tremor. The use of the proposed method can lead to a more precise measurement of motor skills and can be Figure 7The frequency of oscillatory movement of a mouse pointer calculated for eight trials for the non-dominant hand. Walecki etal.: Analysis of tremor in motor learning task51 Table 10Probabilities for post-hoc tests. Distance {1} 39.148 {2} 31.562 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0163 0.0006 0.0014 0.0000 0.0000 0.0000 {3} 27.901 0.0000 0.0163 0.2934 0.4180 0.0081 0.0022 0.0001 {4} 26.307 0.0000 0.0006 0.2934 0.8094 0.1074 0.0433 0.0051 {5} 26.672 0.0000 0.0014 0.4180 0.8094 0.0644 0.0239 0.0024 {6} 23.860 0.0000 0.0000 0.0081 0.1074 0.0644 0.6789 0.2283 {7} 23.232 0.0000 0.0000 0.0022 0.0433 0.0239 0.6789 0.4285 {8} 22.030 0.0000 0.0000 0.0001 0.0051 0.0024 0.2283 0.4285 LSD test, variable: `distance' (non-dominant hand). Error: between MS = 50.513, df = 301.00. Table 11Probabilities for post-hoc tests. TOT {1} 43.337 {2} 52.739 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0492 0.0048 0.0241 0.0000 0.0000 0.0000 {3} 57.106 0.0000 0.0492 0.3882 0.7711 0.0023 0.0002 0.0000 {4} 59.017 0.0000 0.0048 0.3882 0.5671 0.0275 0.0037 0.0001 {5} 57.750 0.0000 0.0241 0.7711 0.5671 0.0056 0.0005 0.0000 {6} 63.916 0.0000 0.0000 0.0023 0.0275 0.0056 0.4802 0.0779 {7} 65.479 0.0000 0.0000 0.0002 0.0037 0.0005 0.4802 0.2889 {8} 67.828 0.0000 0.0000 0.0000 0.0001 0.0000 0.0779 0.2889 LSD test, variable: `TOT' (non-dominant hand). Error: between MS = 107.57, df = 301.00. Table 12Probabilities for post-hoc tests. Tremor {1} 5.2795 {2} 6.3818 0.0012 0.0012 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0231 0.1645 0.0281 0.0000 0.0000 0.0000 {3} 7.1515 0.0000 0.0231 0.3742 0.9391 0.0355 0.0066 0.0000 {4} 6.8515 0.0000 0.1645 0.3742 0.4165 0.0029 0.0003 0.0000 {5} 7.1258 0.0000 0.0281 0.9391 0.4165 0.0294 0.0052 0.0000 {6} 7.8636 0.0000 0.0000 0.0355 0.0029 0.0294 0.5326 0.0253 {7} 8.0742 0.0000 0.0000 0.0066 0.0003 0.0052 0.5326 0.1057 {8} 8.6212 0.0000 0.0000 0.0000 0.0000 0.0000 0.0253 0.1057 LSD test, variable: `Tremor' (non-dominant hand). Error: between MS = 2.4999, df = 301.00. operationalized better. The study showed improvement in procedural task and the relationship between tremor level and performance level. The better performance was related to reduction of large-amplitude and low-frequency tremor and increased low-amplitude and high-frequency tremor. Received December 18, 2012; revised January 25, 2013; accepted January 28, 2013

Journal

Bio-Algorithms and Med-Systems – de Gruyter

Published: Mar 1, 2013

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